量子加速约束规划
Quantum-accelerated constraint programming
摘要：
约束规划（CP）是一种用于建模和解决约束满足和组合优化问题的范式。在CP中，问题用描述可接受的解决方案的约束来建模，并用逻辑推理增强的回溯树搜索来解决。在本文中，我们展示了量子算法如何在推理和搜索两个层面上加速CP。利用现有的量子算法，我们介绍了一个量子加速过滤算法的$\texttt{alldifferent}$的全局约束，并讨论其适用于更广泛的家庭具有类似的结构的全局约束。我们提出的框架内的经典和量子回溯搜索方案，包括一种新的混合经典量子回溯搜索方法的量子滤波算法的集成。这项工作表明，CP是早期容错量子计算机及以后的一个有前途的候选应用。
英文摘要：
Constraint programming (CP) is a paradigm used to model and solve constraint satisfaction and combinatorial optimization problems. In CP, problems are modeled with constraints that describe acceptable solutions and solved with backtracking tree search augmented with logical inference. In this paper, we show how quantum algorithms can accelerate CP, at both the levels of inference and search. Leveraging existing quantum algorithms, we introduce a quantum-accelerated filtering algorithm for the $\texttt{alldifferent}$ global constraint and discuss its applicability to a broader family of global constraints with similar structure. We propose frameworks for the integration of quantum filtering algorithms within both classical and quantum backtracking search schemes, including a novel hybrid classical-quantum backtracking search method. This work suggests that CP is a promising candidate application for early fault-tolerant quantum computers and beyond.
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